Christoph Goebel

Using ICT-Controlled Plug-in Electric Vehicles to Supply Grid Regulation in California at Different Renewable Integration Levels

The purpose of this paper is to quantify the potential for plug-in electric vehicles (PEVs) to meet operating reserve requirements associated with increased deployment of wind and solar generation. The paper advances prior PEV estimates in three key ways. First, we employ easily implementable scheduling strategies with very low centralized computing requirements. Second, we estimate PEV availability based on data sampled from the National Household Travel Survey (NHTS). Third, we predict regulation demand on a per minute basis using published models from the California ISO for 20% and 33% renewable electricity supply. Our key results are as follows: First, the amount of regulation up and regulation down energy delivered by PEVs can be balanced by using a hybrid of two scheduling strategies. Second, the percentage of regulation energy that can be delivered with PEVs is always significantly higher than the percentage of conventional regulation power capacity that is deferred by PEVs. Third, regulation up is harder to satisfy with PEVs than regulation down. Fourth, the scheduling strategies we employ here have a limited impact on load following requirements. Our results indicate that 3 million PEVs could satisfy a significant portion-but not all-of the regulation energy and capacity requirements that are anticipated in California in 2020.

Alternating Direction Method of Multipliers for decentralized electric vehicle charging control

The integration of Electric Vehicles (EVs) into the power grid is a challenging task. From the control perspective, one of the main challenges is the definition of a comprehensive control structure that is scalable to large EV numbers. This paper makes two key contributions: (i) It defines the EV ADMM framework for decentralized EV charging control. (ii) It evaluates EV ADMM using actual data and various EV fleet control problems. EV ADMM is a decentralized optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM). It separates the centralized optimal fleet charging problem into individual optimization problems for the EVs plus one aggregator problem that optimizes fleet goals. Since the individual problems are coupled, they are solved consistently by passing incentive signals between them. The framework can be parameterized to trade-off the importance of fleet goals versus individual EV goals, such that aspects like battery lifetime can be considered. We show how EV ADMM can be applied to control an EV fleet to achieve goals such as demand valley filling and minimal-cost charging. Due to its flexibility and scalability, EV ADMM offers a practicable solution for optimal EV fleet control.

Household electricity demand forecasting: benchmarking state-of-the-art methods

We benchmark state-of-the-art methods for forecasting electricity demand on the household level. Our evaluation is based on two data sets containing the power usage on the individual appliance level. Our results indicate that without further refinement the considered advanced state-of-the-art forecasting methods rarely beat corresponding persistence forecasts. Therefore, we also provide an exploration of promising directions for future research.

Energy Informatics - Current and Future Research Directions

Due to the increasing importance of producing and consuming energy more sustainably, Energy Informatics (EI) has evolved into a thriving research area within the CS/IS community. The article attempts to characterize this young and dynamic field of research by describing current EI research topics and methods and provides an outlook of how the field might evolve in the future. It is shown that two general research questions have received the most attention so far and are likely to dominate the EI research agenda in the coming years: How to leverage information and communication technology (ICT) to (1) improve energy efficiency, and (2) to integrate decentralized renewable energy sources into the power grid. Selected EI streams are reviewed, highlighting how the respective research questions are broken down into specific research projects and how EI researchers have made contributions based on their individual academic background.

The potential of smart home sensors in forecasting household electricity demand

The aim of this paper is to quantify the impact of disaggregated electric power measurements on the accuracy of household demand forecasts. Demand forecasting on the household level is regarded as an essential mechanism for matching distributed power generation and demand in smart power grids. We use state-of-the-art forecasting tools, in particular support vector machines and neural networks, to evaluate the use of disaggregated smart home sensor data for household-level demand forecasting. Our investigation leverages high resolution data from 3 private households collected over 30 days. Our key results are as follows: First, by comparing the accuracy of the machine learning based forecasts with a persistence forecast we show that advanced forecasting methods already yield better forecasts, even when carried out on aggregated household consumption data that could be obtained from smart meters (1-7%). Second, our comparison of forecasts based on disaggregated data from smart home sensors with the persistence and smart meter benchmarks reveals further forecast improvements (4-33%). Third, our sensitivity analysis with respect to the time resolution of data shows that more data only improves forecasting accuracy up to a certain point. Thus, having more sensors appears to be more valuable than increasing the time resolution of measurements.

EPCIS-Based Supply Chain Event Management A Quantitative Comparison of Candidate System Architectures

Supply Chain Event Management (SCEM) systems are decision support systems that allow for monitoring, prioritizing and reacting to events pertaining to the flow of goods in a supply chain. A major current trend in logistics concerns the tracking of materials by using the Radio Frequency Identification (RFID) technology. RFID reader data can be contextualized in the form of standardized supply chain events and exchanged along the supply chain in order to provide the informational basis of SCEM. Using analytical methods, we evaluate two candidate system architectures that enable the cross-company exchange of supply chain events with respect to efficiency and reliability: Event Pull proposed by the industry consortium EPCglobal and Event Push. Event Push is shown to be particularly suitable for the realization of SCEM applications.

Distributed Convex Optimization for Electric Vehicle Aggregators

One of the main challenges for electric vehicle (EV) aggregators is the definition of a control infrastructure that scales to large EV numbers. This paper proposes a new optimization framework for achieving computational scalability based on the alternating directions method of multipliers, which allows for distributing the optimization process across several servers/cores. We demonstrate the performance and versatility of our framework by applying it to two relevant aggregator objectives: 1) valley filling; and 2) cost-minimal charging with grid capacity constraints. Our results show that the solving time of our approach scales linearly with the number of controlled EVs and outperforms the centralized optimization benchmark as the fleet size becomes larger.

RFID IN THE SUPPLY CHAIN: HOW TO OBTAIN A POSITIVE ROI - The Case of Gerry Weber

Although the use of Radio Frequency Identification (RFID) in supply chains still lags behind expectations, its appeal to practitioners and researchers alike is unbowed. Apart from technical challenges such as low read rates and efficient backend integration, a major reason for its slow adoption is the high transponder price. We deliver a case study that investigates the financial, technical and organizational challenges faced by an apparel company that is currently introducing item-level RFID to monitor their supply chain. The company has developed an implementation strategy based on cross-company closed-loop multi-functional use of RFID transponders. This strategy leads to a positive ROI in their case and could serve as an example for other companies considering the introduction of item-level RFID.

Bringing Distributed Energy Storage to Market

Spatially distributed energy storage devices can provide additional flexibility to system operators, which is needed to transition from primarily fossil fuel based electricity generation to variable renewable generation. Aggregators in charge of controlling distributed energy storage can take advantage of existing economic incentives for more flexibility. However, controlling large numbers of energy storage devices with individual constraints in accordance with the strict rules of existing energy and reserve markets is challenging. The purpose of this paper is to investigate the design and performance of a system that enables aggregators to bring large numbers of dedicated and fully controllable energy storage devices to multiple markets concurrently. In particular, we propose algorithms and heuristic optimization methods that allow aggregators to control such energy resources in accordance with arbitrary market rules and participation strategies. Our evaluation is based on a realistic dual market (reserve and intra-day energy) use case. We find that effective market-conform control of large numbers of energy storage devices using the proposed algorithms is feasible, even on short time scales. Furthermore, our results also indicate that the scalability of the proposed system design can be further improved via parallelization without limiting the reserve/energy brought to market.

Model-Based Dispatch Strategies for Lithium-Ion Battery Energy Storage Applied to Pay-as-Bid Markets for Secondary Reserve

Due to their decreasing cost, lithium-ion batteries (LiB) are becoming increasingly attractive for grid-scale applications. In this paper, we investigate the use of LiB for providing secondary reserve and show how the achieved cost savings could be increased by using model-based optimization techniques. In particular, we compare a maximum use dispatch strategy with two different cost-minimizing strategies. For the estimation of state-dependent battery usage cost, we combine an existing electro-thermal LiB model of a mature lithium-iron-phosphate battery cell with corresponding semiempirical calendar and cycle aging models. We estimate the benefit of storage operation from the system operators point of view by gauging the avoided cost of activated reserve. Our evaluation is based on two years worth of data from the German reserve market. The proposed cost minimizing dispatch strategies yield significantly better results than a dispatch strategy that maximizes battery utilization.